HP wanted to establish a presence at conferences in the open source technology realm. They sought after a technical and creative team to help them showcase their work with Open Source technologies. Read more →

HP wanted to establish a presence at conferences in the open source technology realm. They sought after a technical and creative team to help them showcase their work with Open Source technologies. Read more →

Kaggle.com is one of the leading platforms for predictive modelling and analytics competitions. Companies and researchers are able to post data sets and real world challenges which invite statisticians and data scientists to compete in building predictive models that best describe future outcomes.

Business Need

Porto Seguro, one of Brazil’s largest auto and homeowner insurance companies, posted a challenge on Kaggle.com to build a model that predicts the probability that a driver will initiate an auto insurance claim within the next year. While Porto Seguro has used machine learning for the past 20 years, they’re looking to Kaggle’s machine learning community to explore new, more powerful methods. A more accurate prediction will allow them to further tailor their prices, and hopefully make auto insurance coverage more accessible to more drivers.

Akvelon’s participation

A group of two Akvelon machine learning engineers and a data scientist enlisted on Kaggle.com decided to compete side-by-side with more than 5,000 teams for the top positions in the leaderboard. The competition submissions are evaluated using Normalized Gini Coefficient. The Gini Coefficient ranges from approximately 0 for random guessing, to approximately 0.5 for a perfect score.

The insurance group provided the same data set to all competition participants as a raw data set with 893,000 rows of 59 distinct data points, with little or no specification as to what the data actually represents. The Akvelon team went through several different iterations to identify the meaning of each of those data points, to cleanse the data and to reduce the number of variables to a manageable subset. If the data model has too many data points with little or no impact on the output (event of the driver filing insurance claim) the resulting model becomes too complex, and has an adverse impact on the performance to generate the proper output in a timely manner.

‘Competitions requires lots of: “try, fail, fail fast then make some changes and try again, repeat the entire process over and over” approaches’

One of the chosen approaches was the t-SNE (t-distributed stochastic neighbor embedding) which is a nonlinear dimensionality reduction technique that is particularly well-suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. Specifically, it models each high-dimensional object by a two or three dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points. The image below shows the result of this analysis.

t-SNE result (yellow dots corresponds to 1, purple – 0)

The most promising alternatives were XGBoost and LightGBM which placed the team onto the top half of the competition leaderboard with a score of ~0.282. The top ranked team had a score of 0.290. Akvelon ‘s team score was 97.2% of the top ranked team. (The higher the score the better, with the maximum value of 0.5). The next step was to adjust the data model parameters such as (depth of the tree, number of estimators, size of the train and validation sets). Those experimentations raised the score to about ~0.284. With a model that was promising, it was the right time to go back and revisit the groomed data set and do a better approach in choosing which data point to include and which one to replace.

Further research and investigations helped the Akvelon team improve the models. Some of the improvements involved including a Neural Network which raised the score to ~0.285, placing the team in the top 2% of the entire competition.

After some additional tweaks, adjustments and learning from the past Kaggle winner teams approaches, the Akvelon team ranked in the top 1% with the score of 0.287 against the leader having 0.291 (98.6% from the top scoring team).

At the final stage of the competition, the organizers used 70% of the complete data set, unseen yet by any of the competitors. Each teams result against this new data set will determine the final score in the Leaderboard.

Porto Seguro Machine Learning Kaggle Competition Final Standings

The Akvelon Team placed 22nd out of more than 5,000 teams in the final standings after applying the complete data set. Finishing the competition in the top 0.5%.

Benefits and Results

Participating in the Porto Seguro Safe Driver prediction competition increased our expertise in the Machine Learning field. The trial and error portion of this project helped us refine data quickly, focusing on accuracy and details in every model. The data also provided a real world data set for a real world issue, so our models could be applied to different situations and everyday occurrences.

Finishing in the top 0.5% is a proud accomplishment when competing against more than 5,000 other teams.

If you would like to learn more about our Machine Learning capabilities and solutions, contact us today!

The Akvelon Team placed 22nd out of more than 5,000 teams in the final standings after applying the complete data set. Finishing the competition in the top 0.5%.

06 May AT&T Featured apps

Client:

AT&T Inc. is the largest provider both of mobile telephony and of fixed telephony in the USA and also provides broadband subscription television services. As of 2012, it is also the 20th largest mobile telecom operator in the world, with over 106 million mobile customers.

Business Need

Features Apps (FA) Widget is preloaded on Android phones and tablets released by AT&T. It’s placed on the tabletop of the devices and contains editorially-controlled carousel highlighting applications and games from AT&T and its content partners.

This version of FA for Android phones has a look and feel of the tablet version and provides an overall better end user experience. The widget encourages user engagement by providing fresh, timely and relevant content right on the home screen of the device. If the user sees a piece of content they would like to learn more about, or download, they might click on it and they are directed to the source storefront (i.e. Google Play) for download.

Akvelon was chosen as the sole contractor for this project. One of the reasons for this is the aggressive timeline Akvelon agreed on and the fact that Akvelon took full responsibility end-to-end for this fixed-bid contract. The project required a collaboration between two of Akvelon’s offices.

They completely reinvented the featured widgets, thank you!

Solution

Featured Apps is a phone-top widget that helps solve the discoverability challenge by enabling users to find the best apps/games from AT&T and 3rd party developers. From a functionality perspective, it is a simple user interface with a streamlined download flow. The widget has left and right arrows that allows the user to scroll through content. If the user sees a piece of content they would like to learn more about, or download, they can click on it and they are directed to the source storefront (i.e. Google Play) for download.

FAv2 is not only to provide the new user interface, but will also create a better end user experience, improve discoverability of useful Android apps, therefore improving customer satisfaction and reduction in support calls. The widget has a simple user interface and a streamlined download flow. It has left and right arrows that allow the user to scroll through content.

The widget consists of 3 different layouts:

3 Up Layout where items are arranged in groups of 3

Image + Text layout where each content item takes up entire panel

1 Up Layout where each content item takes up entire panel

Benefits and Results

Akvelon created a new user interface providing a better end user experience of the widget. The improved discoverability of useful Android apps is to increase customer satisfaction and reduce in support calls. Akvelon built a redesigned FAv2 widget to ensure that the application meets all the functional and nonfunctional requirements, as well as provides a consistent, optimized user interface on all the required screen configurations.